系统科学与系统工程学报(英文版)
繫統科學與繫統工程學報(英文版)
계통과학여계통공정학보(영문판)
JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING
2007年
2期
166-180
,共15页
Credit rating%business intelligence%localized generalization error%multiple classifier system%feature grouping
Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank's loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today's complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system (embedded in a multiple intelligent agent system) is proposed to predict the financial health of a company. In our model, each individual agent (classifier) makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge (represented by features) about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.